In this paper we discuss policy iteration methods for approximate solution of a finite-state discounted Markov decision problem, with a focus on feature-based aggregation methods and their connection with deep reinfor...In this paper we discuss policy iteration methods for approximate solution of a finite-state discounted Markov decision problem, with a focus on feature-based aggregation methods and their connection with deep reinforcement learning schemes. We introduce features of the states of the original problem, and we formulate a smaller "aggregate" Markov decision problem, whose states relate to the features. We discuss properties and possible implementations of this type of aggregation, including a new approach to approximate policy iteration. In this approach the policy improvement operation combines feature-based aggregation with feature construction using deep neural networks or other calculations. We argue that the cost function of a policy may be approximated much more accurately by the nonlinear function of the features provided by aggregation, than by the linear function of the features provided by neural networkbased reinforcement learning, thereby potentially leading to more effective policy improvement.展开更多
Accurate estimation of photovoltaic(PV)parameters is essential for optimizing solar module perfor-mance and enhancing resource efficiency in renewable energy systems.This study presents a process innovation by introdu...Accurate estimation of photovoltaic(PV)parameters is essential for optimizing solar module perfor-mance and enhancing resource efficiency in renewable energy systems.This study presents a process innovation by introducing,for the first time,the Triangulation Topology Aggregation Optimizer(TTAO)integrated with parallel computing to address PV parameter estimation challenges.The effectiveness and robustness of TTAO are rigorously evaluated using two standard benchmark datasets(KC200GT and R.T.C.France solar cells)and a real-world dataset(Poly70W solar module)under single-,double-,and triple-diode configurations.Results show that TTAO consistently achieves superior accuracy by producing the lowest RMSE values and faster convergence compared to state-of-the-art metaheuristic algorithms.In addition,the integration of parallel computing significantly enhances computational efficiency,reducing execution time by up to 85%without compromising accuracy.Validation using real-world data further demonstrates TTAO’s adaptability and practical relevance in renewable energy systems,effectively bridging the gap between theoretical modeling and real-world implementation for PV system monitoring and optimization,contributing to climate mitigation through improved solar energy performance.展开更多
Accurate medical diagnosis,which involves identifying diseases based on patient symptoms,is often hindered by uncertainties in data interpretation and retrieval.Advanced fuzzy set theories have emerged as effective to...Accurate medical diagnosis,which involves identifying diseases based on patient symptoms,is often hindered by uncertainties in data interpretation and retrieval.Advanced fuzzy set theories have emerged as effective tools to address these challenges.In this paper,new mathematical approaches for handling uncertainty in medical diagnosis are introduced using q-rung orthopair fuzzy sets(q-ROFS)and interval-valued q-rung orthopair fuzzy sets(IVq-ROFS).Three aggregation operators are proposed in our methodologies:the q-ROF weighted averaging(q-ROFWA),the q-ROF weighted geometric(q-ROFWG),and the q-ROF weighted neutrality averaging(qROFWNA),which enhance decision-making under uncertainty.These operators are paired with ranking methods such as the similarity measure,score function,and inverse score function to improve the accuracy of disease identification.Additionally,the impact of varying q-rung values is explored through a sensitivity analysis,extending the analysis beyond the typical maximum value of 3.The Basic Uncertain Information(BUI)method is employed to simulate expert opinions,and aggregation operators are used to combine these opinions in a group decisionmaking context.Our results provide a comprehensive comparison of methodologies,highlighting their strengths and limitations in diagnosing diseases based on uncertain patient data.展开更多
Recently,large-scale deep learning models have been increasingly adopted for point cloud classification.However,thesemethods typically require collecting extensive datasets frommultiple clients,which may lead to priva...Recently,large-scale deep learning models have been increasingly adopted for point cloud classification.However,thesemethods typically require collecting extensive datasets frommultiple clients,which may lead to privacy leaks.Federated learning provides an effective solution to data leakage by eliminating the need for data transmission,relying instead on the exchange of model parameters.However,the uneven distribution of client data can still affect the model’s ability to generalize effectively.To address these challenges,we propose a new framework for point cloud classification called Federated Dynamic Aggregation Selection Strategy-based Multi-Receptive Field Fusion Classification Framework(FDASS-MRFCF).Specifically,we tackle these challenges with two key innovations:(1)During the client local training phase,we propose a Multi-Receptive Field Fusion Classification Model(MRFCM),which captures local and global structures in point cloud data through dynamic convolution and multi-scale feature fusion,enhancing the robustness of point cloud classification.(2)In the server aggregation phase,we introduce a Federated Dynamic Aggregation Selection Strategy(FDASS),which employs a hybrid strategy to average client model parameters,skip aggregation,or reallocate local models to different clients,thereby balancing global consistency and local diversity.We evaluate our framework using the ModelNet40 and ShapeNetPart benchmarks,demonstrating its effectiveness.The proposed method is expected to significantly advance the field of point cloud classification in a secure environment.展开更多
Tetrasulfonated Zn,Co,Ga,In,TiO and metal free phthalocyanine (MTsPc) mainly exist as dimers on the nanostructured TiO 2 electrode and mostly exist as monomers in dimethyl sulfoxide. The photocurrent action spectra ...Tetrasulfonated Zn,Co,Ga,In,TiO and metal free phthalocyanine (MTsPc) mainly exist as dimers on the nanostructured TiO 2 electrode and mostly exist as monomers in dimethyl sulfoxide. The photocurrent action spectra of the liquid junction based on the MTsPc/TiO 2 nanostructured electrode show that only the absorbance of the MTsPc monomer contributes to a photocurrent while that of the MTsPc face to face dimer does not generate a photocurrent.展开更多
Dear Editor,This letter focuses on the remaining useful life(RUL)prediction task under limited labeled samples.Existing machine-learning-based RUL prediction methods for this task usually pay attention to mining degra...Dear Editor,This letter focuses on the remaining useful life(RUL)prediction task under limited labeled samples.Existing machine-learning-based RUL prediction methods for this task usually pay attention to mining degradation information to improve the prediction accuracy of degradation value or health indicator for the next epoch.However,they ignore the cumulative prediction error caused by iterations before reaching the failure point.展开更多
Amphiphiles,including surfactants,have emerged as indispensable elements in materials science and pharmaceutical science,and their functions are highly relying on the critical micelle concentration(CMC)[1,2].Numerous ...Amphiphiles,including surfactants,have emerged as indispensable elements in materials science and pharmaceutical science,and their functions are highly relying on the critical micelle concentration(CMC)[1,2].Numerous fluorimetry-based probes have been developed to measure CMCs[3](Fig.S1).However,CMC measurements using these probes suffer from a time-consuming and laborious procedure and large uncertainties,primarily due to their poor photo-stabilities and highly fluctuating fluorescence backgrounds.展开更多
Dear Editor,Through distributed machine learning,multi-UAV systems can achieve global optimization goals without a centralized server,such as optimal target tracking,by leveraging local calculation and communication w...Dear Editor,Through distributed machine learning,multi-UAV systems can achieve global optimization goals without a centralized server,such as optimal target tracking,by leveraging local calculation and communication with neighbors.In this work,we implement the stochastic gradient descent algorithm(SGD)distributedly to optimize tracking errors based on local state and aggregation of the neighbors'estimation.However,Byzantine agents can mislead neighbors,causing deviations from optimal tracking.We prove that the swarm achieves resilient convergence if aggregated results lie within the normal neighbors'convex hull,which can be guaranteed by the introduced centerpoint-based aggregation rule.In the given simulated scenarios,distributed learning using average,geometric median(GM),and coordinate-wise median(CM)based aggregation rules fail to track the target.Compared to solely using the centerpoint aggregation method,our approach,which combines a pre-filter with the centroid aggregation rule,significantly enhances resilience against Byzantine attacks,achieving faster convergence and smaller tracking errors.展开更多
In wireless sensor networks, secure data aggregation protocols target the two major objectives, namely, security and en route aggregation. Although en route aggregation of reverse multi-cast traffic improves energy ef...In wireless sensor networks, secure data aggregation protocols target the two major objectives, namely, security and en route aggregation. Although en route aggregation of reverse multi-cast traffic improves energy efficiency, it becomes a hindrance to end-to-end security. Concealed data aggregation protocols aim to preserve the end-to-end privacy of sensor readings while performing en route aggregation. However, the use of inherently malleable privacy homomorphism makes these protocols vulnerable to active attackers. In this paper, we propose an integrity and privacy preserving end-to-end secure data aggregation protocol. We use symmetric key-based homomorphic primitives to provide end-to-end privacy and end-to-end integrity of reverse multicast traffic. As sensor network has a non-replenishable energy supply, the use of symmetric key based homomorphic primitives improves the energy efficiency and increase the sensor network’s lifetime. We comparatively evaluate the performance of the proposed protocol to show its efficacy and efficiency in resource-constrained environments.展开更多
Wireless Sensor Networks (WSNs) typically use in-network processing to reduce the communication overhead. Due to the fusion of data items sourced at different nodes into a single one during in-network processing, the ...Wireless Sensor Networks (WSNs) typically use in-network processing to reduce the communication overhead. Due to the fusion of data items sourced at different nodes into a single one during in-network processing, the sanctity of the aggregated data needs to be ensured. Especially, the data integrity of the aggregated result is critical as any malicious update to it can jeopardize not one, but many sensor readings. In this paper, we analyse three different approaches to providing integrity support for SDA in WSNs. The first one is traditional MAC, in which each leaf node and intermediate node share a key with parent (symmetric key). The second is aggregate MAC (AMAC), in which a base station shares a unique key with all the other sensor nodes. The third is homomorphic MAC (Homo MAC) that is purely symmetric key-based approach. These approaches exhibit diverse trade-off in resource consumption and security assumptions. Adding together to that, we also propose a probabilistic and improved variant of homomorphic MAC that improves the security strength for secure data aggregation in WSNs. We carry out simulations in TinyOS environment to experimentally evaluate the impact of each of these on the resource consumption in WSNs.展开更多
Platelet-free plasma of human blood (sodium citrate and EDTA as an anticoagulant) and serum were stored at 4°C, room temperature (25°C) and at 37°C for 24 hours. RBC aggregation decreased after incubati...Platelet-free plasma of human blood (sodium citrate and EDTA as an anticoagulant) and serum were stored at 4°C, room temperature (25°C) and at 37°C for 24 hours. RBC aggregation decreased after incubation of plasma and serum at 37°C for 4 hours. The RBC shape was changed at the same time: discocytes transformed to echinocytes. Storage of plasma and serum at 4°C and room temperature did not lead to significant alterations of RBC aggregation. The RBC shape did not change in influence of such plasma and serum. The most considerable decrease of RBC aggregation and change of their shapes were observed in the plasma and serum incubated at 37°C for 24 hours. Dilution of incubated plasma by fresh plasma led to consistent restoration of erythrocyte shape and their aggregation.展开更多
The challenge of achieving situational understanding is a limiting factor in effective, timely, and adaptive cyber-security analysis. Anomaly detection fills a critical role in network assessment and trend analysis, b...The challenge of achieving situational understanding is a limiting factor in effective, timely, and adaptive cyber-security analysis. Anomaly detection fills a critical role in network assessment and trend analysis, both of which underlie the establishment of comprehensive situational understanding. To that end, we propose a cyber security data warehouse implemented as a hierarchical graph of aggregations that captures anomalies at multiple scales. Each node of our proposed graph is a summarization table of cyber event aggregations, and the edges are aggregation operators. The cyber security data warehouse enables domain experts to quickly traverse a multi-scale aggregation space systematically. We describe the architecture of a test bed system and a summary of results on the IEEE VAST 2012 Cyber Forensics data.展开更多
The simplified neutrosophic set(SNS) is a useful generalization of the fuzzy set that is designed for some practical situations in which each element has different truth membership function, indeterminacy membership f...The simplified neutrosophic set(SNS) is a useful generalization of the fuzzy set that is designed for some practical situations in which each element has different truth membership function, indeterminacy membership function and falsity membership function. In this paper, we develop a series of power aggregation operators called simplified neutrosophic number power weighted averaging(SNNPWA) operator, simplified neutrosophic number power weighted geometric(SNNPWG) operator, simplified neutrosophic number power ordered weighted averaging(SNNPOWA) operator and simplified neutrosophic number power ordered weighted geometric(SNNPOWG) operator. We present some useful properties of the operators and discuss the relationships among them. Moreover, an approach to multiattribute group decision making(MAGDM) within the framework of SNSs is developed by the above aggregation operators.Finally, a practical application of the developed approach to deal with the problem of investment is given, and the result shows that our approach is reasonable and effective in dealing with uncertain decision making problems.展开更多
Finding appropriate flotation reagents to separate copper-nickel sulfide ores from various magnesium silicate gangue minerals has always been a challenge in the mineral processing industry.This study introduced xantha...Finding appropriate flotation reagents to separate copper-nickel sulfide ores from various magnesium silicate gangue minerals has always been a challenge in the mineral processing industry.This study introduced xanthan gum(XG)as a non-toxic and environmentally friendly depressant of talc,olivine,and serpentine.The effects and mechanisms of XG on the aggregation and flotation behavior of talc,olivine and serpentine were investigated by flotation tests,sedimentation tests,IC-FBRM particle size analysis tests,adsorption quantity tests,Fourier transform infrared spectroscopy(FTIR)tests,X-ray photoelectron spectroscopy(XPS)analysis tests and Zeta potential tests.The flotation results indicated that when the three minerals were mixed,XG caused the talc-serpentine aggregation in the solution to shift to olivine-serpentine aggregation,with the remaining XG adsorbing on talc to depress its flotation.In addition,combining XPS and zeta potential tests,the-OH(hydroxyl)groups in XG molecules preferentially adsorbed on Mg sites on the surface of olivine through chemical bonding.The surface potential of olivine significantly shifted to a more negative value,with the negative charge on the olivine surface far exceeding that on the talc surface.This resulted in an increased aggregation effect between positively charged serpentine and negatively charged olivine due to enhanced electrostatic forces.展开更多
The author [Pakkar, M.S. (2014) Using Data Envelopment Analysis and Analytic Hierarchy Process to Construct Composite Indicators. Journal of Applied Operational Research, 6(3), 174-187.] recently proposed a multiplica...The author [Pakkar, M.S. (2014) Using Data Envelopment Analysis and Analytic Hierarchy Process to Construct Composite Indicators. Journal of Applied Operational Research, 6(3), 174-187.] recently proposed a multiplicative approach using Data Envelopment Analysis (DEA) and Analytic Hierarchy Process (AHP) to reflect the priority weights of indicators in constructing composite indicators (CIs). Nonetheless, this approach is limited to the situations with a single level hierarchy which might not satisfy the needs of a multiple level hierarchy. Therefore, the current paper extends this approach to the situations in which the indicators of similar characteristics can be grouped into sub-categories and further linked into categories to form a three-level hierarchical structure. An illustrative example of road safety performance for a set of European countries highlights the usefulness of the proposed “extended approach”.展开更多
In Wireless Sensor Networks (WSNs), sensor nodes are developed densely. They have limit processing ca-pability and low power resources. Thus, energy is one of most important constraints in these networks. In some appl...In Wireless Sensor Networks (WSNs), sensor nodes are developed densely. They have limit processing ca-pability and low power resources. Thus, energy is one of most important constraints in these networks. In some applications of sensor networks, sensor nodes sense data from the environment periodically and trans-mit these data to sink node. In order to decrease energy consumption and so, increase network’s lifetime, volume of transmitted data should be decreased. A solution, which is suggested, is aggregation. In aggrega-tion mechanisms, the nodes aggregate received data and send aggregated result instead of raw data to sink, so, the volume of the transmitted data is decreased. Aggregation algorithms should construct aggregation tree and transmit data to sink based on this tree. In this paper, we propose an automaton based algorithm to con-struct aggregation tree by using energy and distance parameters. Automaton is a decision-making machine that is able-to-learn. Since network’s topology is dynamic, algorithm should construct aggregation tree peri-odically. In order to aware nodes of topology and so, select optimal path, routing packets must be flooded in entire network that led to high energy consumption. By using automaton machine which is in interaction with environment, we solve this problem based on automat learning. By using this strategy, aggregation tree is reconstructed locally, that result in decreasing energy consumption. Simulation results show that the pro-posed algorithm has better performance in terms of energy efficiency which increase the network lifetime and support better coverage.展开更多
Aim To evaluate the time-effect and dose-effect of prasugrel hydrobromide acetic acid compound （PHAAC） inhibiting platelet aggregation. Methods For the time-effect study, 190 Sprague-Dawley （SD） rats were devided ...Aim To evaluate the time-effect and dose-effect of prasugrel hydrobromide acetic acid compound （PHAAC） inhibiting platelet aggregation. Methods For the time-effect study, 190 Sprague-Dawley （SD） rats were devided into 19 groups （n- 10）： the vehicle control group, the PHAAC groups （0.5, 1, 2, 4, 6, 24, 48, 72, 96 h） and the prasugrel hydrochloride groups （0.5, 1, 2, 4, 6, 24, 48, 72, 96 h）. Rats were singly intra- gastic administration of the vehicle, the PHAAC （5 mg·kg^-1） or the prasugrel hydrochloride （5 mg · kg^-1 ）, re- spectively. Blood samples were taken at each time point for the determination of platelet aggregation rate （PAR）. For the dose-effect study, 110 SD rats were devided into 11 groups （n= 10）： the vehicle control group, the PHAAC groups （10, 5, 2.5, 1, 0.5 mg · kg^-1, dosage of prasugrel） and the prasugrel hydrochloride groups （ 10, 5, 2.5, 1, 0.5 mg · kg^-1, dosage of prasugrel） . Blood samples were taken at 4 h after drug administration for the determination of PAR. Results Compared with the vehicle group, PHAAC has significant anti-platelet ag- gregative effects （P 〈 0.05） at the time of 0.5, 1, 2, 4, 6, 24, 48 h, and the effect at the time of 4 h was the strongest. There were no obvious differences between the effect of PHAAC （5 mg · kg^-1） and prasugrel hydrochlo- ride （5 mg · kg^-1） at each time point. Compared with the vehicle group, intragastic administration of PHAAC at the doses of 10, 5, 2.5, 1, 0.5 mg · kg^-1 could obviously inhibite the platelet aggregation, and showed a dose- dependent manner. There were no significant differences between the effect of PHAAC and prasugrel hydrochloride at the same dose. Conclusion PHAAC can inhibit platelet aggregation in a dose-dependent manner, and the effect at 4 h after drug administration is the strongest. The action strength and duration of PHAAC are similar with that of the prasugrel hydrochloride.展开更多
Determining the rate of asphaltene particle growth is one of the main problems in modeling of asphaltene precipitation and deposition.In this paper,the kinetics of asphaltene aggregation under different precipitant co...Determining the rate of asphaltene particle growth is one of the main problems in modeling of asphaltene precipitation and deposition.In this paper,the kinetics of asphaltene aggregation under different precipitant concentrations have been studied.The image processing method was performed on the digital photographs that were taken by a microscope as a function of time to determine the asphaltene aggregation growth mechanisms.The results of image processing by MATLAB software revealed that the growth of asphaltene aggregates is strongly a function of time.Different regions could be recognized during asphaltene particle growth including reaction-and diffusion-limited aggregation followed by reaching the maximum asphaltene aggregate size and start of asphaltene settling and the final equilibrium.Modeling has been carried out to predict the growth of asphaltene particle size based on the fractal theory.General equations have been developed for kinetics of asphaltene aggregation for reaction-limited aggregation and diffusion-limited aggregation.The maximum size of asphaltene aggregates and settling time were modeled by using force balance,acting on asphaltene particles.Results of modeling show a good agreement between laboratory measurements and model calculations.展开更多
The soil environment is linked to aboveground management including plant species composition, grazing intensity, levels of soil disturbance, residue management, and the length of time of a living plant is growing. Soi...The soil environment is linked to aboveground management including plant species composition, grazing intensity, levels of soil disturbance, residue management, and the length of time of a living plant is growing. Soil samples were collected under rangeland [native grass, rotational grazing (NGRG);tame grass, heavy grazing (TGRG);and tame grass, rotational grazing (TGHG)] and cropland [conventional till (CT);CT plus manure (CTM);and long term no till (NT)] systems. The rangeland systems were hypothesized to have higher glomalin content [measured as Bradford-reactive soil protein (BRSP)] and water stable aggregation (WSA) than the cropland systems. In addition, within both rangeland and cropland systems, BRSP and WSA were expected to decline with increased disturbance due to grazing or tillage and going from native to introduced plant species. Differences were detected for BRSP with NGRG and CTM having the highest values in range and cropland systems, respectively. However, the CTM system had higher BRSP values than one or both of the tame grass systems while the CT and NT systems had similar values. Correlation analysis showed strong relationships between all of the BRSP values and WSA.展开更多
文摘In this paper we discuss policy iteration methods for approximate solution of a finite-state discounted Markov decision problem, with a focus on feature-based aggregation methods and their connection with deep reinforcement learning schemes. We introduce features of the states of the original problem, and we formulate a smaller "aggregate" Markov decision problem, whose states relate to the features. We discuss properties and possible implementations of this type of aggregation, including a new approach to approximate policy iteration. In this approach the policy improvement operation combines feature-based aggregation with feature construction using deep neural networks or other calculations. We argue that the cost function of a policy may be approximated much more accurately by the nonlinear function of the features provided by aggregation, than by the linear function of the features provided by neural networkbased reinforcement learning, thereby potentially leading to more effective policy improvement.
基金funded by the Malaysian Ministry of Higher Education through the Fundamental Research Grant Scheme(FRGS/1/2024/ICT02/UCSI/02/1).
文摘Accurate estimation of photovoltaic(PV)parameters is essential for optimizing solar module perfor-mance and enhancing resource efficiency in renewable energy systems.This study presents a process innovation by introducing,for the first time,the Triangulation Topology Aggregation Optimizer(TTAO)integrated with parallel computing to address PV parameter estimation challenges.The effectiveness and robustness of TTAO are rigorously evaluated using two standard benchmark datasets(KC200GT and R.T.C.France solar cells)and a real-world dataset(Poly70W solar module)under single-,double-,and triple-diode configurations.Results show that TTAO consistently achieves superior accuracy by producing the lowest RMSE values and faster convergence compared to state-of-the-art metaheuristic algorithms.In addition,the integration of parallel computing significantly enhances computational efficiency,reducing execution time by up to 85%without compromising accuracy.Validation using real-world data further demonstrates TTAO’s adaptability and practical relevance in renewable energy systems,effectively bridging the gap between theoretical modeling and real-world implementation for PV system monitoring and optimization,contributing to climate mitigation through improved solar energy performance.
文摘Accurate medical diagnosis,which involves identifying diseases based on patient symptoms,is often hindered by uncertainties in data interpretation and retrieval.Advanced fuzzy set theories have emerged as effective tools to address these challenges.In this paper,new mathematical approaches for handling uncertainty in medical diagnosis are introduced using q-rung orthopair fuzzy sets(q-ROFS)and interval-valued q-rung orthopair fuzzy sets(IVq-ROFS).Three aggregation operators are proposed in our methodologies:the q-ROF weighted averaging(q-ROFWA),the q-ROF weighted geometric(q-ROFWG),and the q-ROF weighted neutrality averaging(qROFWNA),which enhance decision-making under uncertainty.These operators are paired with ranking methods such as the similarity measure,score function,and inverse score function to improve the accuracy of disease identification.Additionally,the impact of varying q-rung values is explored through a sensitivity analysis,extending the analysis beyond the typical maximum value of 3.The Basic Uncertain Information(BUI)method is employed to simulate expert opinions,and aggregation operators are used to combine these opinions in a group decisionmaking context.Our results provide a comprehensive comparison of methodologies,highlighting their strengths and limitations in diagnosing diseases based on uncertain patient data.
基金supported in part by the National Key Research and Development Program of Chinaunder(Grant 2021YFB3101100)in part by the National Natural Science Foundation of Chinaunder(Grant 42461057),(Grant 62272123),and(Grant 42371470)+1 种基金in part by the Fundamental Research Program of Shanxi Province under(Grant 202303021212164)in part by the Postgraduate Education Innovation Program of Shanxi Province under(Grant 2024KY474).
文摘Recently,large-scale deep learning models have been increasingly adopted for point cloud classification.However,thesemethods typically require collecting extensive datasets frommultiple clients,which may lead to privacy leaks.Federated learning provides an effective solution to data leakage by eliminating the need for data transmission,relying instead on the exchange of model parameters.However,the uneven distribution of client data can still affect the model’s ability to generalize effectively.To address these challenges,we propose a new framework for point cloud classification called Federated Dynamic Aggregation Selection Strategy-based Multi-Receptive Field Fusion Classification Framework(FDASS-MRFCF).Specifically,we tackle these challenges with two key innovations:(1)During the client local training phase,we propose a Multi-Receptive Field Fusion Classification Model(MRFCM),which captures local and global structures in point cloud data through dynamic convolution and multi-scale feature fusion,enhancing the robustness of point cloud classification.(2)In the server aggregation phase,we introduce a Federated Dynamic Aggregation Selection Strategy(FDASS),which employs a hybrid strategy to average client model parameters,skip aggregation,or reallocate local models to different clients,thereby balancing global consistency and local diversity.We evaluate our framework using the ModelNet40 and ShapeNetPart benchmarks,demonstrating its effectiveness.The proposed method is expected to significantly advance the field of point cloud classification in a secure environment.
文摘Tetrasulfonated Zn,Co,Ga,In,TiO and metal free phthalocyanine (MTsPc) mainly exist as dimers on the nanostructured TiO 2 electrode and mostly exist as monomers in dimethyl sulfoxide. The photocurrent action spectra of the liquid junction based on the MTsPc/TiO 2 nanostructured electrode show that only the absorbance of the MTsPc monomer contributes to a photocurrent while that of the MTsPc face to face dimer does not generate a photocurrent.
基金supported in part by the National Natural Science Foundation of China(U2034209)the Postdoctoral Science Foundation of Chongqing(cstc2021jcyj-bsh X0047)+1 种基金the Fundamental Research Funds for the Central Universities(2022CDJJMRH-008)the National Natural Science Foundation of China(62203075)
文摘Dear Editor,This letter focuses on the remaining useful life(RUL)prediction task under limited labeled samples.Existing machine-learning-based RUL prediction methods for this task usually pay attention to mining degradation information to improve the prediction accuracy of degradation value or health indicator for the next epoch.However,they ignore the cumulative prediction error caused by iterations before reaching the failure point.
基金supported by Shanghai Municipal Commission of Science and Technology,China(Grant No.:19XD1400300)the National Natural Science Foundation of China(Grant Nos.:821040821,82273867,and 82030107).
文摘Amphiphiles,including surfactants,have emerged as indispensable elements in materials science and pharmaceutical science,and their functions are highly relying on the critical micelle concentration(CMC)[1,2].Numerous fluorimetry-based probes have been developed to measure CMCs[3](Fig.S1).However,CMC measurements using these probes suffer from a time-consuming and laborious procedure and large uncertainties,primarily due to their poor photo-stabilities and highly fluctuating fluorescence backgrounds.
基金supported By Guangdong Major Project of Basic and Applied Basic Research(2023B0303000009)Guangdong Basic and Applied Basic Research Foundation(2024A1515030153,2025A1515011587)+1 种基金Project of Department of Education of Guangdong Province(2023ZDZX4046)Shen-zhen Natural Science Fund(Stable Support Plan Program 20231122121608001),Ningbo Municipal Science and Technology Bureau(ZX2024000604).
文摘Dear Editor,Through distributed machine learning,multi-UAV systems can achieve global optimization goals without a centralized server,such as optimal target tracking,by leveraging local calculation and communication with neighbors.In this work,we implement the stochastic gradient descent algorithm(SGD)distributedly to optimize tracking errors based on local state and aggregation of the neighbors'estimation.However,Byzantine agents can mislead neighbors,causing deviations from optimal tracking.We prove that the swarm achieves resilient convergence if aggregated results lie within the normal neighbors'convex hull,which can be guaranteed by the introduced centerpoint-based aggregation rule.In the given simulated scenarios,distributed learning using average,geometric median(GM),and coordinate-wise median(CM)based aggregation rules fail to track the target.Compared to solely using the centerpoint aggregation method,our approach,which combines a pre-filter with the centroid aggregation rule,significantly enhances resilience against Byzantine attacks,achieving faster convergence and smaller tracking errors.
文摘In wireless sensor networks, secure data aggregation protocols target the two major objectives, namely, security and en route aggregation. Although en route aggregation of reverse multi-cast traffic improves energy efficiency, it becomes a hindrance to end-to-end security. Concealed data aggregation protocols aim to preserve the end-to-end privacy of sensor readings while performing en route aggregation. However, the use of inherently malleable privacy homomorphism makes these protocols vulnerable to active attackers. In this paper, we propose an integrity and privacy preserving end-to-end secure data aggregation protocol. We use symmetric key-based homomorphic primitives to provide end-to-end privacy and end-to-end integrity of reverse multicast traffic. As sensor network has a non-replenishable energy supply, the use of symmetric key based homomorphic primitives improves the energy efficiency and increase the sensor network’s lifetime. We comparatively evaluate the performance of the proposed protocol to show its efficacy and efficiency in resource-constrained environments.
文摘Wireless Sensor Networks (WSNs) typically use in-network processing to reduce the communication overhead. Due to the fusion of data items sourced at different nodes into a single one during in-network processing, the sanctity of the aggregated data needs to be ensured. Especially, the data integrity of the aggregated result is critical as any malicious update to it can jeopardize not one, but many sensor readings. In this paper, we analyse three different approaches to providing integrity support for SDA in WSNs. The first one is traditional MAC, in which each leaf node and intermediate node share a key with parent (symmetric key). The second is aggregate MAC (AMAC), in which a base station shares a unique key with all the other sensor nodes. The third is homomorphic MAC (Homo MAC) that is purely symmetric key-based approach. These approaches exhibit diverse trade-off in resource consumption and security assumptions. Adding together to that, we also propose a probabilistic and improved variant of homomorphic MAC that improves the security strength for secure data aggregation in WSNs. We carry out simulations in TinyOS environment to experimentally evaluate the impact of each of these on the resource consumption in WSNs.
文摘Platelet-free plasma of human blood (sodium citrate and EDTA as an anticoagulant) and serum were stored at 4°C, room temperature (25°C) and at 37°C for 24 hours. RBC aggregation decreased after incubation of plasma and serum at 37°C for 4 hours. The RBC shape was changed at the same time: discocytes transformed to echinocytes. Storage of plasma and serum at 4°C and room temperature did not lead to significant alterations of RBC aggregation. The RBC shape did not change in influence of such plasma and serum. The most considerable decrease of RBC aggregation and change of their shapes were observed in the plasma and serum incubated at 37°C for 24 hours. Dilution of incubated plasma by fresh plasma led to consistent restoration of erythrocyte shape and their aggregation.
文摘The challenge of achieving situational understanding is a limiting factor in effective, timely, and adaptive cyber-security analysis. Anomaly detection fills a critical role in network assessment and trend analysis, both of which underlie the establishment of comprehensive situational understanding. To that end, we propose a cyber security data warehouse implemented as a hierarchical graph of aggregations that captures anomalies at multiple scales. Each node of our proposed graph is a summarization table of cyber event aggregations, and the edges are aggregation operators. The cyber security data warehouse enables domain experts to quickly traverse a multi-scale aggregation space systematically. We describe the architecture of a test bed system and a summary of results on the IEEE VAST 2012 Cyber Forensics data.
基金supported by the National Natural Science Foundation of China(11401084)Harbin Science Technology Innovation Talent Research Fund(2016RQQXJ230)
文摘The simplified neutrosophic set(SNS) is a useful generalization of the fuzzy set that is designed for some practical situations in which each element has different truth membership function, indeterminacy membership function and falsity membership function. In this paper, we develop a series of power aggregation operators called simplified neutrosophic number power weighted averaging(SNNPWA) operator, simplified neutrosophic number power weighted geometric(SNNPWG) operator, simplified neutrosophic number power ordered weighted averaging(SNNPOWA) operator and simplified neutrosophic number power ordered weighted geometric(SNNPOWG) operator. We present some useful properties of the operators and discuss the relationships among them. Moreover, an approach to multiattribute group decision making(MAGDM) within the framework of SNSs is developed by the above aggregation operators.Finally, a practical application of the developed approach to deal with the problem of investment is given, and the result shows that our approach is reasonable and effective in dealing with uncertain decision making problems.
基金Project(52264022)supported by the National Natural Science Foundation of ChinaProject(BGRIMM-KJSKL-2025-17)supported by the Open Foundation of State Key Laboratory of Mineral Processing,China。
文摘Finding appropriate flotation reagents to separate copper-nickel sulfide ores from various magnesium silicate gangue minerals has always been a challenge in the mineral processing industry.This study introduced xanthan gum(XG)as a non-toxic and environmentally friendly depressant of talc,olivine,and serpentine.The effects and mechanisms of XG on the aggregation and flotation behavior of talc,olivine and serpentine were investigated by flotation tests,sedimentation tests,IC-FBRM particle size analysis tests,adsorption quantity tests,Fourier transform infrared spectroscopy(FTIR)tests,X-ray photoelectron spectroscopy(XPS)analysis tests and Zeta potential tests.The flotation results indicated that when the three minerals were mixed,XG caused the talc-serpentine aggregation in the solution to shift to olivine-serpentine aggregation,with the remaining XG adsorbing on talc to depress its flotation.In addition,combining XPS and zeta potential tests,the-OH(hydroxyl)groups in XG molecules preferentially adsorbed on Mg sites on the surface of olivine through chemical bonding.The surface potential of olivine significantly shifted to a more negative value,with the negative charge on the olivine surface far exceeding that on the talc surface.This resulted in an increased aggregation effect between positively charged serpentine and negatively charged olivine due to enhanced electrostatic forces.
文摘The author [Pakkar, M.S. (2014) Using Data Envelopment Analysis and Analytic Hierarchy Process to Construct Composite Indicators. Journal of Applied Operational Research, 6(3), 174-187.] recently proposed a multiplicative approach using Data Envelopment Analysis (DEA) and Analytic Hierarchy Process (AHP) to reflect the priority weights of indicators in constructing composite indicators (CIs). Nonetheless, this approach is limited to the situations with a single level hierarchy which might not satisfy the needs of a multiple level hierarchy. Therefore, the current paper extends this approach to the situations in which the indicators of similar characteristics can be grouped into sub-categories and further linked into categories to form a three-level hierarchical structure. An illustrative example of road safety performance for a set of European countries highlights the usefulness of the proposed “extended approach”.
文摘In Wireless Sensor Networks (WSNs), sensor nodes are developed densely. They have limit processing ca-pability and low power resources. Thus, energy is one of most important constraints in these networks. In some applications of sensor networks, sensor nodes sense data from the environment periodically and trans-mit these data to sink node. In order to decrease energy consumption and so, increase network’s lifetime, volume of transmitted data should be decreased. A solution, which is suggested, is aggregation. In aggrega-tion mechanisms, the nodes aggregate received data and send aggregated result instead of raw data to sink, so, the volume of the transmitted data is decreased. Aggregation algorithms should construct aggregation tree and transmit data to sink based on this tree. In this paper, we propose an automaton based algorithm to con-struct aggregation tree by using energy and distance parameters. Automaton is a decision-making machine that is able-to-learn. Since network’s topology is dynamic, algorithm should construct aggregation tree peri-odically. In order to aware nodes of topology and so, select optimal path, routing packets must be flooded in entire network that led to high energy consumption. By using automaton machine which is in interaction with environment, we solve this problem based on automat learning. By using this strategy, aggregation tree is reconstructed locally, that result in decreasing energy consumption. Simulation results show that the pro-posed algorithm has better performance in terms of energy efficiency which increase the network lifetime and support better coverage.
文摘Aim To evaluate the time-effect and dose-effect of prasugrel hydrobromide acetic acid compound （PHAAC） inhibiting platelet aggregation. Methods For the time-effect study, 190 Sprague-Dawley （SD） rats were devided into 19 groups （n- 10）： the vehicle control group, the PHAAC groups （0.5, 1, 2, 4, 6, 24, 48, 72, 96 h） and the prasugrel hydrochloride groups （0.5, 1, 2, 4, 6, 24, 48, 72, 96 h）. Rats were singly intra- gastic administration of the vehicle, the PHAAC （5 mg·kg^-1） or the prasugrel hydrochloride （5 mg · kg^-1 ）, re- spectively. Blood samples were taken at each time point for the determination of platelet aggregation rate （PAR）. For the dose-effect study, 110 SD rats were devided into 11 groups （n= 10）： the vehicle control group, the PHAAC groups （10, 5, 2.5, 1, 0.5 mg · kg^-1, dosage of prasugrel） and the prasugrel hydrochloride groups （ 10, 5, 2.5, 1, 0.5 mg · kg^-1, dosage of prasugrel） . Blood samples were taken at 4 h after drug administration for the determination of PAR. Results Compared with the vehicle group, PHAAC has significant anti-platelet ag- gregative effects （P 〈 0.05） at the time of 0.5, 1, 2, 4, 6, 24, 48 h, and the effect at the time of 4 h was the strongest. There were no obvious differences between the effect of PHAAC （5 mg · kg^-1） and prasugrel hydrochlo- ride （5 mg · kg^-1） at each time point. Compared with the vehicle group, intragastic administration of PHAAC at the doses of 10, 5, 2.5, 1, 0.5 mg · kg^-1 could obviously inhibite the platelet aggregation, and showed a dose- dependent manner. There were no significant differences between the effect of PHAAC and prasugrel hydrochloride at the same dose. Conclusion PHAAC can inhibit platelet aggregation in a dose-dependent manner, and the effect at 4 h after drug administration is the strongest. The action strength and duration of PHAAC are similar with that of the prasugrel hydrochloride.
文摘Determining the rate of asphaltene particle growth is one of the main problems in modeling of asphaltene precipitation and deposition.In this paper,the kinetics of asphaltene aggregation under different precipitant concentrations have been studied.The image processing method was performed on the digital photographs that were taken by a microscope as a function of time to determine the asphaltene aggregation growth mechanisms.The results of image processing by MATLAB software revealed that the growth of asphaltene aggregates is strongly a function of time.Different regions could be recognized during asphaltene particle growth including reaction-and diffusion-limited aggregation followed by reaching the maximum asphaltene aggregate size and start of asphaltene settling and the final equilibrium.Modeling has been carried out to predict the growth of asphaltene particle size based on the fractal theory.General equations have been developed for kinetics of asphaltene aggregation for reaction-limited aggregation and diffusion-limited aggregation.The maximum size of asphaltene aggregates and settling time were modeled by using force balance,acting on asphaltene particles.Results of modeling show a good agreement between laboratory measurements and model calculations.
文摘The soil environment is linked to aboveground management including plant species composition, grazing intensity, levels of soil disturbance, residue management, and the length of time of a living plant is growing. Soil samples were collected under rangeland [native grass, rotational grazing (NGRG);tame grass, heavy grazing (TGRG);and tame grass, rotational grazing (TGHG)] and cropland [conventional till (CT);CT plus manure (CTM);and long term no till (NT)] systems. The rangeland systems were hypothesized to have higher glomalin content [measured as Bradford-reactive soil protein (BRSP)] and water stable aggregation (WSA) than the cropland systems. In addition, within both rangeland and cropland systems, BRSP and WSA were expected to decline with increased disturbance due to grazing or tillage and going from native to introduced plant species. Differences were detected for BRSP with NGRG and CTM having the highest values in range and cropland systems, respectively. However, the CTM system had higher BRSP values than one or both of the tame grass systems while the CT and NT systems had similar values. Correlation analysis showed strong relationships between all of the BRSP values and WSA.
